Systems and methods for accurate and rapid positron emission tomography using deep learning

ABSTRACT

A computer-implemented method is provided for improving image quality with shortened acquisition time. The method comprises: determining an accelerated image acquisition parameter for imaging a subject using a medical imaging apparatus; acquiring, using the medical imaging apparatus, a medical image of the subject according to the accelerated image acquisition parameter; applying a deep network model to the medical image to generate a corresponding transformed medical image with improved quality; and combining the medical image and the corresponding transformed medial image using an adaptive mixing algorithm to generate output image.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.62/891,062 filed on Aug. 23, 2019, the content of which is incorporatedherein in its entirety.

BACKGROUND

Positron Emission Tomography (PET) is a medical imaging techniquecommonly used in applications such as cancer diagnosis, tumor detectionand early diagnosis of neurological disorders. PET provides clinicalimages with high specificity at cellular level. PET uses small amountsof radiotracers to provide functional imaging. Combined with ComputedTomography (CT) or Magnetic Resonance (MR), PET (PET/CT or PET/MR) iswidely applied in clinics for diagnosis of cancers, cardiovasculardiseases, neurological disorders, and other disorders, as well asassessment of the effectiveness of treatment plans. Compared with othermodalities (e.g., X-ray, CT or ultrasound) PET usually takes longertime, sometimes tens of minutes, for data acquisition to generateclinically useful images. PET image quality depends on collecting asufficient number of coincidence events from annihilation photon pairs.Undesirable imaging artifacts as well as misplacement of events in spacemay appear due to the long scan time and the undesired movement ofpatient during the scan. The prolonged acquisition time may lead toinaccuracies in PET radiotracer quantification. The lengthy exam timemay also make the procedure uncomfortable for patients who havedifficulty staying still. Such long scan time for PET exams may resultin high imaging cost and limit the patient volume and accessibility.

SUMMARY

The present disclosure provides improved Positron Emission Tomography(PET) systems and methods that can address various drawbacks ofconventional systems, including those recognized above. Methods andsystems of the presenting disclosure capable of providing improved imagequality with shortened image acquisition time. In particular, PET imageswith improved quality may be obtained at shortened acquisition timewithout losing quantification accuracy.

Traditionally, short scan duration may result in low counting statisticsin the image frame and image reconstruction from the low-countprojection data can be challenging due to the tomography is ill-posedand high noise. The provided methods and systems may significantlyreduce PET scan time by applying deep learning techniques so as tomitigate imaging artifacts and improve image quality. Examples artifactsin medical imaging may include noise (e.g., low signal noise ratio),blur (e.g., motion artifact), shading (e.g., blockage or interferencewith sensing), missing information (e.g., missing pixels or voxels inpainting due to removal of information or masking), and/orreconstruction (e.g., degradation in the measurement domain).

The methods and systems provided herein may allow for faster PET imagingacquisition while preserving quantification accuracy related tophysiological or biochemical information. For example, methods andsystems of the present disclosure may provide accelerated PET imageacquisition while preserving accuracy in PET uptake quantification. Insome embodiments, accurate PET quantification with acceleratedacquisition may be achieved by leveraging machine learning techniquesand the biochemical information (e.g., radioactivity distribution) inthe original input PET data using an adaptive mixing algorithm for imagereconstruction.

In an aspect, a computer-implemented method is provided for improvingimage quality with shortened acquisition time. The method comprises: (a)acquiring, using a medical imaging apparatus, a medical image of asubject, wherein the medical image is acquired using an acceleratedimage acquisition parameter; (b) applying a deep network model to themedical image to generate a corresponding transformed medical image withimproved quality; and (d) combining the medical image and thecorresponding transformed medical image to generate an output image.

In another related yet separated aspect, a non-transitorycomputer-readable storage medium including instructions that, whenexecuted by one or more processors, cause the one or more processors toperform operations. The operations comprise: (a) acquiring, using amedical imaging apparatus, a medical image of a subject, wherein themedical image is acquired using an accelerated image acquisitionparameter; (b) applying a deep network model to the medical image togenerate a corresponding transformed medical image with improvedquality; and (d) combining the medical image and the correspondingtransformed medical image to generate an output image.

In some embodiments, the medical image and the corresponding transformedmedical image are dynamically combined based at least in part on anaccuracy of the medical image. In other embodiments, the medical imageand the corresponding transformed medical image are spatially combined.In some cases, the medical image and the corresponding transformedmedical image are combined using ensemble averaging.

In some embodiments, the medical image and the corresponding transformedmedical image are combined using an adaptive mixing algorithm. In somecases, the adaptive mixing algorithm comprises calculating a weightingcoefficient for the medical image and the corresponding transformedmedical image. In some instances, the weighting coefficient iscalculated based on one or more parameters quantifying an accuracy ofthe transformed medical image. For example, the one or more parametersquantifying the accuracy of the transformed medical image is selectedfrom the group consisting of standardized uptake value (SUV), local peakvalue of SUV, maximum value of SUV, and mean value of SUV.Alternatively, the weighting coefficient is calculated based on both animage quality and quantification accuracy of the medical image and thecorresponding transformed medical image.

In some embodiments, the medical image is Positron Emission Tomography(PET) image.

Additionally, methods and systems of the present disclosure may beapplied to existing systems without a need of a change of the underlyinginfrastructure. In particular, the provided methods and systems mayaccelerate PET scan time at no additional cost of hardware component andcan be deployed regardless of the configuration or specification of theunderlying infrastructure.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and descriptions are to be regarded as illustrative in nature,and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.To the extent publications and patents or patent applicationsincorporated by reference contradict the disclosure contained in thespecification, the specification is intended to supersede and/or takeprecedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings (also “figure” and “FIG.” herein) of which:

FIG. 1 shows an example of a workflow for processing and reconstructingPET image data.

FIG. 2 shows an example method for improving the performance of methodsand systems described consistent herein.

FIG. 3 schematically illustrates an exemplary PET platform in which animaging accelerator can be implemented.

FIG. 4 shows a block diagram of an exemplary PET imaging acceleratorsystem, in accordance with embodiments of the present disclosure.

FIG. 5 illustrates an example of method for improving PET image qualityand preserving quantification accuracy with accelerated acquisition.

FIG. 6 shows PET images taken under standard acquisition time (ScenarioA), with accelerated acquisition (Scenario B), and the image innScenario B processed by the provided methods and systems (Scenario C).

FIG. 7 shows examples of image quality metrics for differentacceleration factors.

FIG. 8 shows examples of accuracy of quantification measure such asstandard uptake value (SUV) using methods and systems in the presentdisclosure.

FIG. 9 shows a comparison between ground-truth image (scenario A), imageenhanced with deep learning without adaptive mixing (scenario B) andenhanced image processed with adaptive mixing (scenario C).

DETAILED DESCRIPTION

While various embodiments of the invention have been shown and describedherein, it will be obvious to those skilled in the art that suchembodiments are provided by way of example only. Numerous variations,changes, and substitutions may occur to those skilled in the art withoutdeparting from the invention. It should be understood that variousalternatives to the embodiments of the invention described herein may beemployed.

Positron Emission Tomography (PET) is a nuclear medicine functionalimaging technique that is used to observe metabolic processes in thebody as an aid to the diagnosis of disease. A PET system may detectpairs of gamma rays emitted indirectly by a positron-emittingradioligand, most commonly fluorine-18, which is introduced into apatient body on a biologically active molecule such as a radioactivetracer. The biologically active molecule can be any suitable type suchas fludeoxyglucose (FDG). With tracer kinetic modeling, PET is capableof quantifying physiologically or biochemically important parameters inregions of interest or voxel-wise to detect disease status andcharacterize severity.

Though positron emission tomography (PET) and PET data examples areprimarily provided herein, it should be understood that the presentapproach may be used in other imaging modality contexts. For instance,the presently described approach may be employed on data acquired byother types of tomographic scanners including, but not limited to,computed tomography (CT), single photon emission computed tomography(SPECT) scanners, functional magnetic resonance imaging (fMRI), ormagnetic resonance imaging (MRI) scanners.

The term “accurate quantification” or “quantification accuracy” of PETimaging may refer to the accuracy of quantitative biomarker assessmentsuch as radioactivity distribution. Various metrics can be employed forquantifying the accuracy of PET image such as standard uptake value(SUV) for an FDG-PET scan. For example, peak SUV value may be used asmetric for quantifying accuracy of the PET image. Other commonstatistics such as mean, median, min, max, range, skewness, kurtosis,and more complex values, such as metabolic volume above an absolute SUVof 5 standardized uptake value (SUV) of 18-FDG, can also be calculatedand used for quantifying the accuracy of PET imaging.

The term “shortened acquisition,” as used herein, generally refers toshortened PET acquisition time or PET scan duration. The providedsystems and methods may be able to achieve PET imaging with improvedimage quality by an acceleration factor of at least 1.5, 2, 3, 4, 5, 10,15, 20, a factor of a value above 20 or below 1.5, or a value betweenany of the two aforementioned values. An accelerated acquisition can beachieved by shortening the scan duration of a PET scanner. For example,an acquisition parameter (e.g., 3 min/bed, 18 min in total) may be setup via the PET system prior to performing a PET scan.

PET images taken under short scan duration may have low image quality(e.g., high noise) due to low coincident-photon counts detected inaddition to various physical degradation factors. Example of sources ofnoise in PET may include scatter (a detected pair of photons, at leastone of which was deflected from its original path by interaction withmatter in the field of view, leading to the pair being assigned to anincorrect line of response (LOR)) and random events (photons originatingfrom two different annihilation events but incorrectly recorded as acoincidence pair because their arrival at their respective detectorsoccurred within a coincidence timing window. Methods and systems of thepresent disclosure may improve the quality of the medical image whilepreserving the quantification accuracy without modification to thephysical system.

FIG. 1 shows an example of a workflow 100 for processing andreconstructing PET image data. As described above, shortened scan timemay be setup in the PET scanner or on the PET system. The acquiredimages 101 under the shortened scan time or faster acquisition may havelower image quality due to reduced scanning time per image. For example,the PET images 101 may have low image resolution and/or signal to noiseratio (SNR) due to the accelerated acquisition as described above. ThePET images 101 may be acquired by complying with an existing orconventional scan protocol such as metabolic volume calibration orinterinstitutional cross-calibration and quality control. The PET images101 may be acquired and reconstructed using any conventionalreconstruction techniques without requiring additional changes to thePET scanner. The PET images 101 acquired with shortened scan durationmay also be referred to as fast-scanned image or original input imagewhich can be used interchangeably throughout the specification.

The aforementioned PET images 101 as obtained by an acceleratedacquisition may have lower image quality. For example, image resolutionand signal to noise ratio (SNR) may be lower due to lowcoincident-photon counts detected in addition to various physicaldegradation factors. The input PET images 101 may suffer from artifactsthat may include noise (e.g., low signal noise ratio), blur (e.g.,motion artifact), shading (e.g., blockage or interference with sensing),missing information (e.g., missing pixels or voxels in painting due toremoval of information or masking), reconstruction (e.g., degradation inthe measurement domain), sharpness and various other artifacts that maylower the quality of the image. In addition to the acceleratedacquisition factor, other sources may also introduce noise in PETimaging which may include scatter (a detected pair of photons, at leastone of which was deflected from its original path by interaction withmatter in the field of view, leading to the pair being assigned to anincorrect LOR) and random events (photons originating from two differentannihilation events but incorrectly recorded as a coincidence pairbecause their arrival at their respective detectors occurred within acoincidence timing window.

The PET images 101 may be reconstructed image obtained using anyexisting reconstruction method. For example, the PET images 101 may bereconstructed using filtered back projection, statistical,likelihood-based approaches, and various other conventional methods.However, the PET images 101 may still have low image quality such as lowresolution and/or low SNR due to the shortened acquisition time andreduced number of detected photons.

Image quality of the fast-scanned PET image 101 may be improved usingdeep learning techniques so that quality enhanced PET images 103 can beachieved. In some embodiments, deep learning techniques may be utilizedfor enhancing the image quality. For instance, during the imageenhancement process 110, a deep learning algorithm may be applied toestimate a function ƒ that transforms the fast-scanned, low qualityimage x_(fast) to a high quality image {tilde over (x)}. For example,the fast-scanned PET images 101 with low image quality may be suppliedto a model network as input, and the output of the model networkcomprises the enhanced PET images. For instance, the enhanced PET image103 generated by the model network may have an improved SNR and/orhigher resolution compared to the original input PET image 101.

A trained deep learning model may be used for transforming the inputimage data with lower quality into PET image data with higher quality.In some embodiments, the input data may be 2D image data. In some cases,the input data may be 3D volume comprising multiple axial slices. Theinput PET images 101 may be sinogram data collected by the PET scanner.With aid of the provided system, higher quality PET images may beobtained with shortened acquisition duration.

The model network may be a trained model for enhancing the quality ofPET images. In some embodiments, the model may include an artificialneural network that can employ any type of neural network model, such asa feedforward neural network, radial basis function network, recurrentneural network, convolutional neural network, deep residual learningnetwork and the like. In some embodiments, the machine learningalgorithm may comprise a deep learning algorithm such as convolutionalneural network (CNN). Examples of machine learning algorithms mayinclude a support vector machine (SVM), a naïve Bayes classification, arandom forest, a deep learning model such as neural network, or othersupervised learning algorithm or unsupervised learning algorithm. Themodel network may be a deep learning network such as CNN that maycomprise multiple layers. For example, the CNN model may comprise atleast an input layer, a number of hidden layers and an output layer. ACNN model may comprise any total number of layers, and any number ofhidden layers. The simplest architecture of a neural network starts withan input layer followed by a sequence of intermediate or hidden layers,and ends with output layer. The hidden or intermediate layers may act aslearnable feature extractors, while the output layer in this exampleprovides PET images with improved quality (e.g., enhanced PET images103). Each layer of the neural network may comprise a number of neurons(or nodes). A neuron receives input that comes either directly from theinput data (e.g., low quality image data, fast-scanned PET data, etc.)or the output of other neurons, and performs a specific operation, e.g.,summation. In some cases, a connection from an input to a neuron isassociated with a weight (or weighting factor). In some cases, theneuron may sum up the products of all pairs of inputs and theirassociated weights. In some cases, the weighted sum is offset with abias. In some cases, the output of a neuron may be gated using athreshold or activation function. The activation function may be linearor non-linear. The activation function may be, for example, a rectifiedlinear unit (ReLU) activation function or other functions such assaturating hyperbolic tangent, identity, binary step, logistic, arcTan,softsign, parameteric rectified linear unit, exponential linear unit,softPlus, bent identity, softExponential, Sinusoid, Sinc, Gaussian,sigmoid functions, or any combination thereof.

In some embodiments, the model for enhancing image quality may betrained using supervised learning. For example, in order to train thedeep learning network, pairs of fast-scanned PET images with low quality(i.e., acquired under reduced time) and standard/high quality PET imagesas ground truth from multiple subjects may be provided as trainingdataset. As described above, the model is trained to approximate atransformation function ƒ that transforms the fast-scanned, low qualityimage x_(fast) to a high quality image {tilde over (x)}. Thehigh-quality output image may be an image with high SNR or highresolution. This function ƒ may be obtained by optimizing metricsbetween the ground truth image x, obtained by standard PET imaging whichis not shortened and the estimated image {tilde over (x)} through atraining process on a number of training datasets.

In some embodiments, the model may be trained using unsupervisedlearning or semi-supervised learning that may not require abundantlabeled data. High quality medical image datasets or paired dataset canbe hard to collect. In some cases, the provided method may utilizeunsupervised training approach allowing the deep learning method totrain and apply on existing datasets (e.g., unpaired dataset) that arealready available in clinical database. In some embodiments, thetraining process of the deep learning model may employ residual learningmethod. In some cases, the network structure can be a combination ofU-net structure and a residual network. Details about the trainingprocess are described later herein.

Next, the enhanced PET image 103 may be further processed for improvingquantification accuracy. In some situations, the enhanced PET images 103may be over-smoothed or losing small structures (e.g., due to the L2norm used as the cost function) during the image quality enhancementprocess. In some embodiments, the enhanced PET image 103 may be fusedwith the original input PET images 101 to generate accurate PET imageswith improved image quality 105. The original input PET images 101 maycontain useful biochemical information (e.g., preserve lesion uptake)despite the lower image quality. Such original biochemical informationmay be utilized by the present method and system for preserving orimproving the quantification accuracy of the PET images. For example,the original biochemical information from the original image may bebeneficially combined with the enhanced PET image thereby allowing foran output PET image with both improved image quality and accuracy.

The method 100 may apply an adaptive mixing algorithm 120 to generateoutput image 105 with both high image quality and high quantificationaccuracy. In some cases, the enhanced PET image data 103 and theoriginal input PET image data 101 may be combined using an adaptivemixing algorithm such that the overall performance of the output imagedata 105 is improved over that of the enhanced image 103 and/or theoriginal input PET image 101. For example, the quantification accuracyof the output image 105 may be improved over that of the enhanced image103 while the image quality is improved over that of the original inputPET image 101. In some cases, the quantification accuracy of the outputimage may be improved over both the enhanced image and the originalinput PET image given that random variations may be reduced during theimage processing such as normalization or filtering.

As shown in FIG. 1, the output enhanced images generated by the neuralnetwork and the original input images may be mixed 120. In some cases,the original input PET images 101 and the enhanced PET images 103 may bemixed in a dynamic fashion. For example, the mixing function may be aweighted combination of the deep learning enhanced image {tilde over(x)} 103 and the original input image x_(fast) 101 where the weights orcoefficient are dynamically calculated based on one or more parametersquantifying the accuracy of PET image. The parameters may be selectedbased on the type of tracer and/or region of interest (ROI). Anysuitable parameters can be selected for quantifying the accuracy of PETimage such as standardized uptake value (SUV) for an FDG-PET scan. Othermetrics such as (local) peak value of SUV, maximum value of SUV, meanvalue of SUV or any combination of the above may be used as parametersfor quantifying accuracy of the PET image and for determining theweighting coefficients. Any common statistics such as mean, median, min,max, range, skewness, kurtosis, and more complex values, such asmetabolic volume above an absolute SUV of 5 standardized uptake value(SUV) of 18-FDG, can also be used for quantifying the accuracy of PETimaging and for determining the weighting coefficients.

The mixing of the enhanced image

103 and the original input image x_(fast) 101 may be spatially and/orintensity adaptive. For example, the adaptive mixing may be achieved byapplying spatially varying filters. The original input image 101 and thequality enhanced images 103 may be spatially averaged. The averagingoperator may be an ensemble averaging. The averaging operator may be aweighted averaging of original input image x_(fast) and the enhancedquality image

in a selected region (e.g., ROI) or across the entire image frame.

In some cases, the output of the adaptive mixing and deep learningsystem y[i,j] 105 can be obtained according to the following formula:

${y\left\lbrack {i,j} \right\rbrack} = {{\sum\limits_{i = 1}^{k}{{{f_{i}(x)}\left\lbrack {i,j} \right\rbrack}{\alpha_{i}\left( {{\phi{{f_{1}(x)}\left\lbrack {i,j} \right\rbrack}},\ldots,{\phi{{f_{k}(x)}\left\lbrack {i,j} \right\rbrack}}} \right)}}} + {\left( {1 - {\sum\alpha_{i}}} \right){x\left\lbrack {i,j} \right\rbrack}}}$

wherein x represents the original input image (e.g., x_(fast)), ƒ_(i)(x)represents the output of the deep learning network (e.g.,

), and α_(i) represents the weighting factor for each of k differentoutputs of the deep learning network. As shown in the above example,each weighting factor α_(i) may be determined based on the output of thedeep learning network ƒ_(i)(x). In some cases, the weighting factor maybe a function of the deep-learning-enhanced image (i.e., output of thedeep learning network). The function can be any transformation functionsuch as a linear or nonlinear max-filter.

The weighting factor value or weighting coefficient can bespatially-adaptive and intensity-adaptive such that the quality andaccuracy of the PET images may be optimized. For example, if the peakvalue of SUV changes significantly in the deep learning enhanced image(compared to the original input image), the original input image may beassigned a greater weighting coefficient in terms of SUV therebypreserving the accuracy feature. In some cases, the adaptive weightingcoefficient for adaptive mixing of images may be determined based on apre-determined relationship to one or more parameters for quantifyingaccuracy (e.g., peak SUV, mean SUV) as described above. For example, theadaptive weighting factors for adaptive mixing of images may be selectedto preserve the maximum value of the quantification factors such as SUV.In some cases, the adaptive weighting coefficient for adaptive mixing ofimages may be determined based on both the metrics on image quality andthe metrics on quantification accuracy. For example, the weightingcoefficient in the weighting average may be determined based on metricson image quality, such as peak signal to noise ratio (PSNR) orstructural similarity index (SSIM), and multi-scale the structuralsimilarity index (MS-SSIM) or normalized root mean square error (NRMSE),and quantification parameters such as SUV or maximum value of SUV.

In some cases, the weighting coefficients may be determined inreal-time. For example, the weighting coefficients may be calculatedupon capturing of a new set of input data. Alternatively or in additionto, the weighting coefficients may be determined per scanner, persystem, per examination, per patient, per model, per a pre-determinedperiod of time and the like. For instance, the weighting coefficient maybe determined by comparing the image quality metrics (e.g., PSNR, SSIM,RMSE) between the ground-truth data and the quality enhanced image whilepreserving the peak-SUV to maintain the local peak value. As an example,the weighting coefficients may be determined by tuning the values tominimize a value of the goal function RMSE (ƒ_(i)(x), ground-truth)+RMSE(max_filter(ƒ_(i)(x)), max_filter(ground-truth)). The weightingcoefficients may be determined during a training phase, after deploymentof the model, after implementing the model network or during a continualimprovement stage of the system.

In some cases, a method may be provided to further improve theperformance of the system. The method may involve pre-processing PETimages prior to quality enhancement using a variety of differentfiltering parameters and identifying the optimal output image. FIG. 2shows an exemplary method 200 for improving the performance of methodsand systems consistent with the disclosed methods and systems describedwith respect to FIG. 1. In the illustrated example, a variety of filtersmay be applied to the low quality images obtained from accelerated PETimages prior to improving the image quality through the deep learningnetwork. Non-limiting examples of these filters can be convolutionalfilters (for example Roberts edge enhancing filter, Gaussian smoothingfilter, Gaussian sharpening filter, Sobel edge detection filter, etc.)or morphological filters (for example erosion, dilation, segmentationfilters, etc.) or various other filters. These filters may enhance imageparameters such as SNR or resolution. Other methods for improving imagequality such as methods making use of local patch statistics, prioranatomical or temporal information, denoising methods, such non-localmean denoising, guided image filtering, entropy or mutual informationbased methods, segmentation based methods and gradient based methods asdescribed above can also be used. As a result, multiple filtered inputimages (e.g., filtered input image 1, . . . m) are created. Eachfiltered input image set may then be processed by the deep learningnetwork and the adaptive mixing methods as described in FIG. 1. As aresult, multiple output candidates may be generated for each image.Next, an optimal output image candidate may be selected from theplurality of output candidates. The selection criterion can be based onimage quality metrics such as SNR, image sharpness, resolution or othersand any combination of the above. In some cases, upon identifying theoptimal output image candidate, the associated filter (e.g., filterparameters), weighting coefficients for adaptive mixing and otherparameters for processing data in the process 200 may be adopted andutilized for processing ongoing input data.

System Overview

The systems and methods can be implemented on an existing PET imagingsystem without a need of a change of hardware infrastructure. FIG. 3schematically illustrates an example PET platform 300 in which animaging accelerator 340 of the presenting disclosure may be implemented.The PET system 300 may comprise a detector ring system 303, a patienttransport table 305 connected to the detector ring system, and acontroller 301 operably coupled to the detector ring system. In oneexample, a patient may lie on the patient transport table 305 and thedetector ring system 303 may surround at least a portion of patient'sbody. The PET platform 300 can be combined with other imaging modalitiesto form, for example, a PET/CT or a PET/MRI platform. Suitable methodssuch as co-registering images may be involved for integrating the PETplatform into another imaging modality (e.g., CT, MRI). The PET platform300 may further comprise a computer system 310 and one or more databasesoperably coupled to the controller 301 over the network 330. Thecomputer system 310 may be used for further implementing the methods andsystems explained above to improve the quality of images.

The controller 301 may be a coincidence processing unit. The controllermay comprise or be coupled to an operator console (not shown) which caninclude input devices (e.g., keyboard) and control panel and a display.For example, the controller may have input/output ports connected to adisplay, keyboard and printer. In some cases, the operator console maycommunicate through the network with the computer system 310 thatenables an operator to control the production and display of images on ascreen of display. The images may be images with improved quality and/oraccuracy acquired according to an accelerated acquisition scheme. Theimage acquisition scheme may be determined automatically by the PETimaging accelerator 340 and/or by a user as described later herein.

The PET system may comprise a user interface. The user interface may beconfigured to receive user input and output information to a user. Theuser input may be related to controlling or setting up an imageacquisition scheme. For example, the user input may indicate scanduration (e.g., the min/bed) for each acquisition or scan time for aframe that determines one or more acquisition parameters for anaccelerated acquisition scheme. The user input may be related to theoperation of the PET system (e.g., certain threshold settings forcontrolling program execution, image reconstruction algorithms, etc).The user interface may include a screen such as a touch screen and anyother user interactive external device such as handheld controller,mouse, joystick, keyboard, trackball, touchpad, button, verbal commands,gesture-recognition, attitude sensor, thermal sensor, touch-capacitivesensors, foot switch, or any other device.

The PET platform 300 may comprise computer systems 310 and databasesystems 320, which may interact with a PET imaging accelerator 340. Thecomputer system may comprise a laptop computer, a desktop computer, acentral server, distributed computing system, etc. The processor may bea hardware processor such as a central processing unit (CPU), a graphicprocessing unit (GPU), a general-purpose processing unit, which can be asingle core or multi core processor, or a plurality of processors forparallel processing. The processor can be any suitable integratedcircuits, such as computing platforms or microprocessors, logic devicesand the like. Although the disclosure is described with reference to aprocessor, other types of integrated circuits and logic devices are alsoapplicable. The processors or machines may not be limited by the dataoperation capabilities. The processors or machines may perform 512 bit,256 bit, 128 bit, 64 bit, 32 bit, or 16 bit data operations. Detailsregarding the computer system are described with respect to FIG. 4.

The PET platform 300 may comprise one or more databases. The one or moredatabases 320 may utilize any suitable database techniques. Forinstance, structured query language (SQL) or “NoSQL” database may beutilized for storing PET image data, raw collected data, reconstructedimage data, training datasets, trained model (e.g., hyper parameters),adaptive mixing weighting coefficients, etc. Some of the databases maybe implemented using various standard data-structures, such as an array,hash, (linked) list, struct, structured text file (e.g., XMVL), table,JSON, NOSQL and/or the like. Such data-structures may be stored inmemory and/or in (structured) files. In another alternative, anobject-oriented database may be used. Object databases can include anumber of object collections that are grouped and/or linked together bycommon attributes; they may be related to other object collections bysome common attributes. Object-oriented databases perform similarly torelational databases with the exception that objects are not just piecesof data but may have other types of functionality encapsulated within agiven object. If the database of the present disclosure is implementedas a data-structure, the use of the database of the present disclosuremay be integrated into another component such as the component of thepresent invention. Also, the database may be implemented as a mix ofdata structures, objects, and relational structures. Databases may beconsolidated and/or distributed in variations through standard dataprocessing techniques. Portions of databases, e.g., tables, may beexported and/or imported and thus decentralized and/or integrated.

The network 330 may establish connections among the components in thePET platform and a connection of the PET system to external systems. Thenetwork 330 may comprise any combination of local area and/or wide areanetworks using both wireless and/or wired communication systems. Forexample, the network 330 may include the Internet, as well as mobiletelephone networks. In one embodiment, the network 330 uses standardcommunications technologies and/or protocols. Hence, the network 330 mayinclude links using technologies such as Ethernet, 802.11, worldwideinteroperability for microwave access (WiMAX), 2G/3G/4G mobilecommunications protocols, asynchronous transfer mode (ATM), InfiniBand,PCI Express Advanced Switching, etc. Other networking protocols used onthe network 330 can include multiprotocol label switching (MPLS), thetransmission control protocol/Internet protocol (TCP/IP), the UserDatagram Protocol (UDP), the hypertext transport protocol (HTTP), thesimple mail transfer protocol (SMTP), the file transfer protocol (FTP),and the like. The data exchanged over the network can be representedusing technologies and/or formats including image data in binary form(e.g., Portable Networks Graphics (PNG)), the hypertext markup language(HTML), the extensible markup language (XML), etc. In addition, all orsome of links can be encrypted using conventional encryptiontechnologies such as secure sockets layers (SSL), transport layersecurity (TLS), Internet Protocol security (IPsec), etc. In anotherembodiment, the entities on the network can use custom and/or dedicateddata communications technologies instead of, or in addition to, the onesdescribed above.

FIG. 4 shows a block diagram of an exemplary PET imaging acceleratorsystem 400, in accordance with embodiments of the present disclosure.The PET imaging accelerator system 400 may comprise multiple components,including but not limited to, a training module 402, an imageenhancement module 404, an interactive PET acquisition module 406 and auser interface module 408.

The training module 402 may be configured to obtain and manage trainingdatasets. For example, the training datasets may comprise pairs ofstandard acquisition and shortened acquisition images from same subject.The training module 402 may be configured to train a deep learningnetwork for enhancing the image quality as described elsewhere herein.For example, the training module may employ supervised training,unsupervised training or semi-supervised training techniques fortraining the model. The training module may be configured to implementthe machine learning methods as described elsewhere herein. The trainingmodule may train a model off-line. Alternatively or additionally, thetraining module may use real-time data as feedback to refine the modelfor improvement or continual training. In some cases, the trainingmodule may implement the method described in FIG. 2 to further improvethe performance of the system by pre-determining a set of parameters forgenerating an optimal output for a given PET scanner, a patient, a test,a period of test time and the like.

The image enhancement module 404 may be configured to enhance imagequality using a trained model obtained from the training module. Theimage enhancement module may implement the trained model for makinginferences, i.e., generating PET images with improved quality. Forinstance, the image enhancement module may take one or more fast-scannedPET image data collected from a PET scanner as input and output PETimage data with improved quality. In some cases, the image enhancementmodule and/or the adaptive mixing and filtering module 406 may implementthe method as described in FIG. 2 to further improve the performance ofthe system.

The interactive PET acquisition module 406 may be operably coupled to acontroller of the PET system. The interactive PET acquisition module 406may be configured to generate an acquisition time or acquisition speed,such as the total duration of imaging, scan duration per frame or theminutes per bed (min/bed) for each acquisition. In some cases, theinteractive PET acquisition module may receive a user input indicating adesired acquisition time (e.g., acquisition speed, etc). in some cases,in response to receiving the target or desired acceleration, theinteractive PET acquisition module may run tests on one or moreacquisition speeds and determine an optimal acquisition speed. Theoptimal acquisition speed may be determined based on a predeterminedrule. For instance, the optimal acquisition speed may be determinedbased on the quality of the output image. For example, an acquisitionspeed meeting the target acceleration speed while providing the bestquality images may be selected. In some cases, the interactive PETacquisition module may allow a user to define an acquisition speed. Inresponse to receiving a user defined acquisition speed, the interactivePET acquisition module may run simulations and generate output imagesassociated with the acquisition speed. A user may or may not furtheradjust the acquisition speed so as to change the quality or othercharacteristics of the output images. The determined acquisition speedmay then be transmitted to the controller of the PET system forcontrolling the operation of the imaging system as described elsewhereherein. In some cases, the interactive PET acquisition module may beoperably coupled to the user interface module 408 for receiving userinput and outputting an auto-generated acquisition speed or simulatedimages. In some cases, the interactive PET acquisition module 406 mayalso be operably coupled to the image enhancement module and/or theadaptive mixing and filtering module 406 for performing simulations asdescribed above.

The computer system 400 may be programmed or otherwise configured tomanage and/or implement an enhanced PET imaging system and itsoperations. The computer system 400 may be programmed to implementmethods consistent with the disclosure herein.

The computer system 400 may include a central processing unit (CPU, also“processor” and “computer processor” herein), a graphic processing unit(GPU), a general-purpose processing unit, which can be a single core ormulti core processor, or a plurality of processors for parallelprocessing. The computer system 400 can also include memory or memorylocation (e.g., random-access memory, read-only memory, flash memory),electronic storage unit (e.g., hard disk), communication interface(e.g., network adapter) for communicating with one or more othersystems, and peripheral devices 435, 320, such as cache, other memory,data storage and/or electronic display adapters. The memory, storageunit, interface and peripheral devices are in communication with the CPUthrough a communication bus (solid lines), such as a motherboard. Thestorage unit can be a data storage unit (or data repository) for storingdata. The computer system 400 can be operatively coupled to a computernetwork (“network”) 330 with the aid of the communication interface. Thenetwork 330 can be the Internet, an internet and/or extranet, or anintranet and/or extranet that is in communication with the Internet. Thenetwork 330 in some cases is a telecommunication and/or data network.The network 330 can include one or more computer servers, which canenable distributed computing, such as cloud computing. The network 330,in some cases with the aid of the computer system 400, can implement apeer-to-peer network, which may enable devices coupled to the computersystem 400 to behave as a client or a server.

The CPU can execute a sequence of machine-readable instructions, whichcan be embodied in a program or software. The instructions may be storedin a memory location, such as the memory. The instructions can bedirected to the CPU, which can subsequently program or otherwiseconfigure the CPU to implement methods of the present disclosure.Examples of operations performed by the CPU can include fetch, decode,execute, and writeback.

The CPU can be part of a circuit, such as an integrated circuit. One ormore other components of the system can be included in the circuit. Insome cases, the circuit is an application specific integrated circuit(ASIC).

The storage unit can store files, such as drivers, libraries and savedprograms. The storage unit can store user data, e.g., user preferencesand user programs. The computer system 300 in some cases can include oneor more additional data storage units that are external to the computersystem, such as located on a remote server that is in communication withthe computer system through an intranet or the Internet.

The computer system 400 can communicate with one or more remote computersystems through the network 330. For instance, the computer system 400can communicate with a remote computer system of a user or aparticipating platform (e.g., operator). Examples of remote computersystems include personal computers (e.g., portable PC), slate or tabletPC's (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones(e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personaldigital assistants. The user can access the computer system 300 via thenetwork 330.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 400, such as, for example, on the memoryor electronic storage unit. The machine executable or machine readablecode can be provided in the form of software. During use, the code canbe executed by the processor. In some cases, the code can be retrievedfrom the storage unit and stored on the memory for ready access by theprocessor. In some situations, the electronic storage unit can beprecluded, and machine-executable instructions are stored on memory.

The code can be pre-compiled and configured for use with a machinehaving a processer adapted to execute the code, or can be compiledduring runtime. The code can be supplied in a programming language thatcan be selected to enable the code to execute in a pre-compiled oras-compiled fashion.

Aspects of the systems and methods provided herein, such as the computersystem 300, can be embodied in programming. Various aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such as memory (e.g., read-only memory, random-accessmemory, flash memory) or a hard disk. “Storage” type media can includeany or all of the tangible memory of the computers, processors or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which may providenon-transitory storage at any time for the software programming. All orportions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into the computer platform of an applicationserver. Thus, another type of media that may bear the software elementsincludes optical, electrical and electromagnetic waves, such as usedacross physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also may be considered as media bearing the software.As used herein, unless restricted to non-transitory, tangible “storage”media, terms such as computer or machine “readable medium” refer to anymedium that participates in providing instructions to a processor forexecution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as may be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediamay take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

The computer system 400 can include or be in communication with anelectronic display 435 that comprises a user interface (UI) 440 forproviding, for example, displaying reconstructed images or acquisitionspeeds. Examples of UI's include, without limitation, a graphical userinterface (GUI) and web-based user interface.

Methods and systems of the present disclosure can be implemented by wayof one or more algorithms. An algorithm can be implemented by way ofsoftware upon execution by the central processing unit. For example,some embodiments use the algorithm illustrated in FIG. 1, FIG. 2 andFIG. 5 or other algorithms provided in the associated descriptionsabove.

FIG. 5 illustrates an exemplary process 500 for improving PET imagequality and/or accuracy with accelerated acquisition. A plurality of PETimages may be obtained from PET imaging system (operation 510) fortraining a deep learning model. The plurality of PET images for forminga training dataset can also be obtained from external data sources(e.g., clinical database, etc).

The PET images may be used to form training datasets (operation 520). Insome embodiments, the training dataset may comprise pairs of relativelylower quality image data from accelerated PET acquisition andcorresponding higher quality image data (i.e., ground truth). In somecases, the training dataset may comprise augmented datasets obtainedfrom simulation. For instance, image data from clinical database may beused to generate low quality image data mimicking the image dataacquired with shortened scan time. In an example, noise may be added toraw image data to mimic image data reconstructed from low-countprojection data. Similarly, higher quality input image data may beobtained from direct image acquisition with longer acquisition time. Insome cases, the higher quality image data (e.g., ground-truth data) maybe obtained using methods described herein to improve the reconstructionperformance. For example, methods based on prior information or longersan time may be employed. Example methods may include but not limitedto, methods making use of local patch statistics, prior anatomical ortemporal information, denoising methods, such as non-local meandenoising, guided image filtering, Gaussian filtering, entropy or mutualinformation based methods, segmentation based methods and gradient basedmethods and the like.

Alternatively or in addition to, un-paired training dataset may beutilized. In some cases, the network model may be trained without orwith little labeled data. For example, the deep learning network modelsmay be trained on both paired datasets and un-paired datasets whichbeneficially provides flexibility in data collection. The trainingdatasets may comprise paired datasets including a reference image withhigh quality and an the corresponding fast-scanned image with lowerquality, and un-paired datasets which may include image data from whichunsupervised features may be extracted. For example, the un-paired datamay be supplied to the model system for training an autoencoder toextract unsupervised features. In some cases, the un-paired dataset maybe used for further enhancing the performance of a model trained onpaired dataset.

The training step 530 may comprise a deep learning algorithm consistentwith the disclosure herein. In some cases, the deep learning algorithmmay be a convolutional neural network. As described above, the trainingprocess may involve supervised learning, unsupervised learning,semi-supervised learning or a combination of the above.

In some cases, the paired datasets may be used in supervised training.In some embodiments, the model for enhancing image quality may betrained using supervised learning. For example, in order to train thedeep learning network, pairs of fast-scanned PET images with low quality(i.e., acquired under reduced time) and standard/high quality PET imagesas ground truth from multiple subjects may be provided as trainingdataset. As described above, the model is trained to approximate atransformation function ƒ that transforms the fast-scanned, low qualityimage x_(fast) to a high quality image {tilde over (x)}. The highquality output image may be high SNR or high resolution image. Thisfunction ƒ may be obtained by optimizing metrics between the groundtruth image x, obtained by standard PET imaging which is not shortenedand the estimated image {tilde over (x)} through a training process on anumber of training datasets.

The loss function or cost function for optimizing the classifier can beof any type of function such as L1 loss (i.e., mean absolute error), L2loss (i.e., mean square error), Lp loss and various other supervisedloss. There can be one or multiple cost metrics which may be combinedwith optimized weightings. g can be any suitable metrics such as l₂ norm∥k(x)−k(

)∥₂, l₁ norm ∥k(x)−k(

)∥₁, structural dissimilarity or other metrics. In some cases, k can beidentity transform then the metrics are calculated in image domain. kcan be any other transforms, such as Fourier transform, therefore themetrics are calculated in corresponding domain. In some cases, the gmetric may be used as criteria during the training process of the deeplearning model. In some cases, the g metrics can also be a network modelthat is separately or simultaneously trained together with f, todiscriminate image states and evaluate image quality. In some cases, thedeep learning model may be trained with adaptively optimized metricsbased on user input and real-time simulated output images.

In some embodiments, the training process of the deep learning model mayemploy a residual learning method. In some instances, the residuallearning framework may be used for evaluating a trained model. In someinstances, the residual learning framework with skip connections maygenerate estimated ground-truth images from the low quality images suchas PET image collected under accelerated acquisition, with refinement toensure it is consistent with measurement (data consistency). In somecases, what the model learns is the residual of the difference betweenthe fast-scanned image data and ground-truth image data, which issparser and less complex to approximate using the network structure. Themethod may use by-pass connections to enable the residual learning. Insome cases, a residual network may be used and the direct model outputmay be the estimated residual/error between the fast-scanned image(i.e., input image) and the enhanced image. In other word, the functionto be learned by the deep learning framework is a residual functionwhich in some situations may be easy to optimize. The enhanced image canbe recovered by adding the input image to the residual. This residualtraining approach may reduce the complexity of training and achievebetter performance where the output level is small.

In some cases, the deep learning model may be trained with adaptivelytuned parameters based on user input and real-time estimated outputimages. Alternatively or in addition to, the deep learning network maybe a “plain” CNN that does not involve residual learning. In some cases,during the training process, the deep learning model may adaptively tunemodel parameters to approximate the enhanced PET image data from aninitial set of the input images, and outputting the enhanced PET imagedata.

In some cases, training the network model may comprise further enhancingthe model using un-paired datasets. In some cases, a supervised learningand unsupervised learning may be performed sequentially. In somesituations unsupervised algorithms may introduce instability duringtraining. To avoid such instability, it is beneficial to train a modelusing supervised training with paired datasets then further enhance themodel using unsupervised learning. For example, the model may beinitially trained to estimate a transformation from a fast-scanner imageto an enhanced image using supervised losses such as pixel-wise L1and/or L2 losses. The performance of the resulting model may not be goodenough due to limitation of the supervised losses and the amount ofavailable paired dataset. The model may be further improved byunsupervised learning or a combination of unsupervised and supervisedlearning. For example, the model can be further refined or enhancedusing refinement losses such as a mixed loss of supervised losses (e.g.,L1 loss, L2 loss, Lp loss, structural similarity, perceptual losses,etc) and unsupervised losses (e.g., GAN (Generative Adversarial Network)loss, least-square GAN, WGAN losses (Wasserstein GAN), etc).

There can be multiple iterations in a training process. In each of themultiple iterations, different supervised losses, unsupervised losses orcombinations of supervised losses and unsupervised losses may beselected. In an iteration of the process, supervised and unsupervisedlearning techniques may be applied sequentially or concurrently. Theun-paired datasets may be used for unsupervised training which enablesthe method to further train and apply on most or all existing PETdatasets. In some cases, the system and/or methods may employ CycleGenerative Adversarial Network (Cycle-GAN) that further enables improvedperformance and more flexible training on both paired datasets andun-paired datasets. A Cycle-GAN may be used in adversarial training inwhich a discriminative network is used to enhance the primary network.The primary network may be generative (segmentation, synthesis) ordiscriminative (classification). The machine learnt network may furtherbe configured as a U-net. The U-net is an auto-encoder in which theoutputs from the encoder-half of the network are concatenated with themirrored counterparts in the decoder-half of the network. The U-net mayreplace pooling operations by upsampling operators thereby increasingthe resolution of the output.

In a step 540, parameters such as weighting coefficients for theadaptive mixing algorithm may be determined. The adaptive mixingalgorithm and the process for determining the weighting coefficients canbe the same as those described elsewhere herein. In some cases, theweighting coefficients may be calculated or determined dynamically inreal-time based on the input PET image data. Alternatively or inaddition to, the weighting coefficients may be calculated based onempirical data or pre-determined prior to implementing the method in thephysical platform. In some cases, the network model and/or theparameters of the adaptive mixing algorithm may be further tuned orrefined with data collected from the PET system after implementation ordeployment. Once the parameters are determined, enhanced images from thetraining step and the fast-scanned images may be adaptively mixed andfiltered in order to obtain images with improved image quality whilepreserving quantification accuracy.

In optional embodiments, an optimal output may be selected by applying avariety of filtering or quality improving methods to the input imagedata. The step of identifying optimal output data (operation 550) and/orthe associated parameters for pre-processing the data prior to applyingthe deep learning model can be the same as those described in FIG. 2.

Although FIG. 5 shows a method in accordance with some embodiments, aperson of ordinary skill in the art will recognize that there are manyadaptations for various embodiments. For example, the operations can beperformed in any order. Some of the operations may be precluded, some ofthe operations may be performed concurrently in one step, some of theoperations may be repeated, and some of the operations may comprisesub-steps of other operations. For example, the parameters of theadaptive mixing algorithm can be determined concurrent with or prior totraining deep learning model. The method may also be modified inaccordance with other aspects of the disclosure as provided herein.

EXAMPLE DATASETS

FIG. 6 shows PET images taken under standard acquisition time (ScenarioA), with accelerated acquisition (Scenario B), and the fast-scannedimage processed by the provided methods and systems (Scenario C).Scenario A shows a standard PET image with no enhancement or shortenedacquisition time. The acquisition time for this example is 2 minutes perbed (min/bed). This image may be used in training the deep learningnetwork as an example of the ground truth. Scenario B shows an exampleof a PET image with shortened acquisition time. In this example theacquisition time is accelerated by 4 times and the acquisition time isreduced to 1 min/bed. The fast-scanned image present lower image qualitysuch as high noise. This image may be an example of the second imageused in pairs of images for training the deep learning network. ScenarioC shows an example of an improved quality image which the methods andsystems of the present disclosure are applied to. The image quality hassubstantially improved and comparable to the standard PET image quality.

Example 1

FIG. 7 shows the analytic results of a study. In this study, sevensubjects (5 males, age: 57:14 years, weight: 81±10 Kgs) referred for awhole-body fludeoxyglucose-18 (FDG-18) PET/CT scan on a GE Discovery 710scanner were recruited for the study following IRB approval and informedconsent. The standard of care was a 2 min/bed PET acquisition acquiredin list-mode. 2-fold, 3-fold, and 4-fold accelerated acquisitions weresynthesized using the first 30 seconds (s), 40 s, and 60 s list-mode PETcounts of the original 2 min acquisition. Quantitative image qualitymetrics such as normalized root-mean-squared-error (NRMSE), peak signalto noise ratio (PSNR), and structural similarity (SSIM) were calculatedfor all enhanced and non-enhanced accelerated PET scans, with thestandard 2 min acquisition as the ground-truth. The results are shown inFIG. 7A-C. Better image quality may be achieved using the methods andsystems in the present disclosure.

Example 2

FIG. 8 shows the results of a study. In this study, 15 subjects (7 male,8 female; mean age: 67 years, range: 45-85 years, average BMI: 30,range: 19-48) referred for clinical whole-body PET/CT exams underwenttwo separate PET scans, one with the standard acquisition duration (3mins/abed) followed by one acquired 4 times faster (45 s/bed), followingIRB approval and informed consent. The 4 times faster PET images wereenhanced using the proposed methods and systems in the presentdisclosure. One nuclear medicine physician reviewed the standardacquisition PET images, identified possible lesions and some normalregions, and drew regions of interest (ROIs). The same lesions werereviewed on the quality enhanced images from the shortened acquisitiontime images and the ROIs from the standard acquisition were propagatedto the deep learning enhanced 4 times faster (deep-learning-enhanced 4×faster) scan.

Quantitative mean and maximum SUV values per ROI between the standardand deep-learning-enhanced four times faster acquisitions werevisualized using Bland-Altman tests and compared using concordancecorrelation coefficients (CCC), linear regressions, and Mann-WhitneyU-Tests. A total of 63 ROIs were identified in the standard acquisitionPET images. The Bland-Altman plot in subplot a and subplot b (dottedline indicating mean, and dashed line indicating 95% limits ofagreement) shows minimal differences between SUVs obtained from the twosets of scans, with almost all values contained within the 95% limits ofagreement interval. CCC and linear Pearson coefficient values of 0.99for both SUV-max and SUV-mean indicates very strong agreement betweenthe SUV values from standard acquisition and deep-learning-enhanced scan(subplot c and subplot d, where the dotted line indicates the unityline). This is further indicated by the lack of statistical significanceof p=0.68 for SUV-max and p=0.77 for SUV-mean values using theMann-Whitney U-Test.

Example 3

FIG. 9 shows a comparison between ground-truth image (scenario A), imageenhanced with deep learning without adaptive mixing (scenario B) andenhanced image processed with adaptive mixing (scenario C). As shown inthe result, the quantification accuracy (e.g., peak SUV) of the qualityenhanced images with adaptive mixing (e.g., scenario C) is better thanthat of the quality enhanced images without adaptive mixing. Forexample, the peak SUV of the ROI in scenario C is 2.626 which is greaterthan the peak SUV 2.532 in the same ROI in scenario B and is closer tothe ground-truth result.

As used herein, “or” is inclusive and not exclusive, unless expresslyindicated otherwise by context. Therefore, “A or B” means “A, B, orboth,” unless expressly indicated otherwise or indicated otherwise bycontext. Moreover, “and” is both joint and several, unless expresslyindicated otherwise or indicated otherwise by context.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. It is not intendedthat the invention be limited by the specific examples provided withinthe specification. While the invention has been described with referenceto the aforementioned specification, the descriptions and illustrationsof the embodiments herein are not meant to be construed in a limitingsense. Numerous variations, changes, and substitutions will now occur tothose skilled in the art without departing from the invention.Furthermore, it shall be understood that all aspects of the inventionare not limited to the specific depictions, configurations or relativeproportions set forth herein which depend upon a variety of conditionsand variables. It should be understood that various alternatives to theembodiments of the invention described herein may be employed inpracticing the invention. It is therefore contemplated that theinvention shall also cover any such alternatives, modifications,variations or equivalents. It is intended that the following claimsdefine the scope of the invention and that methods and structures withinthe scope of these claims and their equivalents be covered thereby.

What is claimed is:
 1. A computer-implemented method for improving imagequality with shortened acquisition time, comprising: (a) acquiring,using a medical imaging apparatus, a medical image of a subject, whereinthe medical image is acquired using an accelerated image acquisitionparameter; (b) applying a deep network model to the medical image togenerate a corresponding transformed medical image with improvedquality; and (c) combining the medical image and the correspondingtransformed medical image to generate an output image.
 2. Thecomputer-implemented method of claim 1, wherein the medical image andthe corresponding transformed medical image are dynamically combinedbased at least in part on an accuracy of the medical image.
 3. Thecomputer-implemented method of claim 1, wherein the medical image andthe corresponding transformed medical image are spatially combined. 4.The computer-implemented method of claim 3, wherein the medical imageand the corresponding transformed medical image are combined usingensemble averaging.
 5. The computer-implemented method of claim 1,wherein the medical image and the corresponding transformed medicalimage are combined using an adaptive mixing algorithm.
 6. Thecomputer-implemented method of claim 5, wherein the adaptive mixingalgorithm comprises calculating a weighting coefficient for the medicalimage and the corresponding transformed medical image.
 7. Thecomputer-implemented method of claim 6, wherein the weightingcoefficient is calculated based on one or more parameters quantifying anaccuracy of the transformed medical image.
 8. The computer-implementedmethod of claim 7, wherein the one or more parameters quantifying theaccuracy of the transformed medical image is selected from the groupconsisting of standardized uptake value (SUV), local peak value of SUV,maximum value of SUV, and mean value of SUV.
 9. The computer-implementedmethod of claim 6, wherein the weighting coefficient is calculated basedon both an image quality and quantification accuracy of the medicalimage and the corresponding transformed medical image.
 10. Thecomputer-implemented method of claim 1, wherein the medical image isPositron Emission Tomography (PET) image.
 11. A non-transitorycomputer-readable storage medium including instructions that, whenexecuted by one or more processors, cause the one or more processors toperform operations comprising: (a) acquiring, using a medical imagingapparatus, a medical image of a subject, wherein the medical image isacquired using an accelerated image acquisition parameter; (b) applyinga deep network model to the medical image to generate a correspondingtransformed medical image with improved quality; and (c) combining themedical image and the corresponding transformed medical image togenerate an output image.
 12. The non-transitory computer-readablestorage medium of claim 11, wherein the medical image and thecorresponding transformed medical image are dynamically combined basedat least in part on an accuracy of the medical image.
 13. Thenon-transitory computer-readable storage medium of claim 11, wherein themedical image and the corresponding transformed medical image arespatially combined.
 14. The non-transitory computer-readable storagemedium of claim 13, wherein the medical image and the correspondingtransformed medical image are combined using ensemble averaging.
 15. Thenon-transitory computer-readable storage medium of claim 11, wherein themedical image and the corresponding transformed medical image arecombined using an adaptive mixing algorithm.
 16. The non-transitorycomputer-readable storage medium of claim 15, wherein the adaptivemixing algorithm comprises calculating a weighting coefficient for themedical image and the corresponding transformed medical image.
 17. Thenon-transitory computer-readable storage medium of claim 16, wherein theweighting coefficient is calculated based on one or more parametersquantifying an accuracy of the transformed medical image.
 18. Thenon-transitory computer-readable storage medium of claim 17, wherein theone or more parameters quantifying the accuracy of the transformedmedical image is selected from the group consisting of standardizeduptake value (SUV), local peak value of SUV, maximum value of SUV, andmean value of SUV.
 19. The non-transitory computer-readable storagemedium of claim 16, wherein the weighting coefficient is calculatedbased on both an image quality and quantification accuracy of themedical image and the corresponding transformed medical image.
 20. Thenon-transitory computer-readable storage medium of claim 11, wherein themedical image is Positron Emission Tomography (PET) image.